To investigate the potential reasons behind our method's improvement over the baselines, we look at the agents' messages sent during evaluation episodes in the Fully-Cooperative Predator-Environment and compare our method against the most performant baseline AE COMM. Specifically, we collect all the messages sent from 7 evaluation episodes and perform clustering on them using DBSCAN [1]}. Figure REF  shows the clustering results for both methods. Our proposed method appears to be able to learn a more coherent and consistent common language as the clustering algorithm detects a smaller number of clusters and noise points with a much higher silhouette coefficient than AE COMM. Silhouette coefficient has a value between -1 and 1 which measures how well defined the clusters are. The higher the value the less overlapping between clusters. One plausible explanation to these results is that AE COMM grounds communication without considering messages of other agents, leading to the formation of multiple protocols between agents within the same message space, while our proposed method CACL induces a more common protocol with the consideration of messages received. The improved performance and speed of our approach further indicate the benefits of learning a common language in the decentralized communication setting.
